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| """ | |
| Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py | |
| """ | |
| import argparse | |
| import os | |
| import sys | |
| import random | |
| import numpy as np | |
| import torch | |
| import torch.backends.cudnn as cudnn | |
| import gradio as gr | |
| from global_local.common.config import Config | |
| from global_local.common.dist_utils import get_rank | |
| from global_local.common.registry import registry | |
| from global_local.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle,conv_llava_llama_2 | |
| import decord | |
| decord.bridge.set_bridge('torch') | |
| #%% | |
| # imports modules for registration | |
| from global_local.datasets.builders import * | |
| from global_local.models import * | |
| from global_local.processors import * | |
| from global_local.runners import * | |
| from global_local.tasks import * | |
| #%% | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="Demo") | |
| #parser.add_argument("--cfg-path", required=True, help="path to configuration file.") | |
| parser.add_argument("--cfg-path", type=str, default='./eval_configs/conversation_demo.yaml', help="path to configuration file.") | |
| parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.") | |
| parser.add_argument("--model_type", type=str, default='llama_v2', help="specify LLM") | |
| parser.add_argument('--pretrained_weight_path', type=str, default="./ckpt/finetuned_model.pth", metavar='PWP', | |
| help='path to pretrained weight path') | |
| parser.add_argument('--num_frames_per_clip', type=int, default=16, metavar='NPPC', | |
| help='specify how frames to use per clip') | |
| parser.add_argument('--num_segments', type=int, default=4, metavar='NS', | |
| help='specify number of video segments') | |
| parser.add_argument('--hierarchical_agg_function', type=str, default="without-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned", metavar='HAF', | |
| help='specify function to merge global and clip visual representations') | |
| parser.add_argument( | |
| "--options", | |
| nargs="+", | |
| help="override some settings in the used config, the key-value pair " | |
| "in xxx=yyy format will be merged into config file (deprecate), " | |
| "change to --cfg-options instead.", | |
| ) | |
| args = parser.parse_args() | |
| return args | |
| def setup_seeds(config): | |
| seed = config.run_cfg.seed + get_rank() | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| cudnn.benchmark = False | |
| cudnn.deterministic = True | |
| # ======================================== | |
| # Model Initialization | |
| # ======================================== | |
| print('Initializing Chat') | |
| args = parse_args() | |
| cfg = Config(args) | |
| model_config = cfg.model_cfg | |
| model_config.device_8bit = args.gpu_id | |
| model_cls = registry.get_model_class(model_config.arch) | |
| model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id)) | |
| model.num_frames_per_clip = args.num_frames_per_clip | |
| model.num_segments = args.num_segments | |
| model.hierarchical_agg_function = args.hierarchical_agg_function | |
| model.global_region_embed_weight = None | |
| model.initialize_visual_agg_function() | |
| best_checkpoint = torch.load(args.pretrained_weight_path, map_location='cpu')['model_state_dict'] | |
| pretrained_dict = {} | |
| for k, v in best_checkpoint.items(): | |
| pretrained_dict[k.replace('module.', '')] = v | |
| model_dict = model.state_dict() | |
| model_dict.update(pretrained_dict) | |
| model.load_state_dict(model_dict) | |
| model.cuda().eval() | |
| #vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train | |
| vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train | |
| vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) | |
| chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id)) | |
| print('Initialization Finished') | |
| # ======================================== | |
| # Gradio Setting | |
| # ======================================== | |
| def gradio_reset(chat_state, img_list): | |
| if chat_state is not None: | |
| chat_state.messages = [] | |
| if img_list is not None: | |
| img_list = [] | |
| return None, gr.update(value=None, interactive=True), gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your video first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list | |
| def upload_imgorvideo(gr_video, gr_img, text_input, chat_state,chatbot): | |
| if args.model_type == 'vicuna': | |
| chat_state = default_conversation.copy() | |
| else: | |
| chat_state = conv_llava_llama_2.copy() | |
| if gr_img is None and gr_video is None: | |
| return None, None, None, gr.update(interactive=True), chat_state, None | |
| elif gr_img is not None and gr_video is None: | |
| print(gr_img) | |
| chatbot = chatbot + [((gr_img,), None)] | |
| chat_state.system = "You are able to understand the visual content that the user provides. Follow the instructions carefully and explain your answers in detail." | |
| img_list = [] | |
| llm_message = chat.upload_img(gr_img, chat_state, img_list) | |
| return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot | |
| elif gr_video is not None and gr_img is None: | |
| print(gr_video) | |
| chatbot = chatbot + [((gr_video,), None)] | |
| chat_state.system = "You are able to understand the visual content that the user provides. Follow the instructions carefully and explain your answers in detail." | |
| img_list = [] | |
| llm_message = chat.upload_video_without_audio(gr_video, chat_state, img_list) | |
| return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot | |
| else: | |
| # img_list = [] | |
| return gr.update(interactive=False), gr.update(interactive=False, placeholder='Currently, only one input is supported'), gr.update(value="Currently, only one input is supported", interactive=False), chat_state, None,chatbot | |
| def gradio_ask(user_message, chatbot, chat_state): | |
| if len(user_message) == 0: | |
| return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state | |
| chat.ask(user_message, chat_state) | |
| chatbot = chatbot + [[user_message, None]] | |
| return '', chatbot, chat_state | |
| def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature): | |
| llm_message = chat.answer(conv=chat_state, | |
| img_list=img_list, | |
| num_beams=num_beams, | |
| temperature=temperature, | |
| max_new_tokens=300, | |
| max_length=2000)[0] | |
| chatbot[-1][1] = llm_message | |
| print(chat_state.get_prompt()) | |
| print(chat_state) | |
| return chatbot, chat_state, img_list | |
| title = """ | |
| <h1 align="center">Global-Local QFormer for Long Video Understanding with LLMs</h1> | |
| <h5 align="center"> Introduction: We introduce a Global-Local QFormer video model that is connected with a Large Language Model to understand \ | |
| and answer questions about long videos. </h5> | |
| <div style='display:flex; gap: 0.25rem; '> | |
| <a href='https://huggingface.co/spaces/rxtan/rxtan/Global-Local-QFormer-Video-LLM'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> | |
| <a href=''><img src='https://img.shields.io/badge/Paper-PDF-red'></a> | |
| </div> | |
| Thank you for using the Global-Local QFormer Demo Page! If you have any questions or feedback, feel free to contact us. | |
| Current online demo uses the 7B version of Llama-2 due to resource limitations. | |
| """ | |
| Note_markdown = (""" | |
| ### We note that our Global-Local QFormer model may be limited at understanding videos from rare domains. Due to the pretraining data, the \ | |
| model may be susceptible to hallucinations | |
| We would like to acknowledge the Video-LLama repository which we copied the demo layout from. | |
| **Boston University** | |
| """) | |
| cite_markdown = (""" | |
| """) | |
| #case_note_upload = (""" | |
| ### We provide some examples at the bottom of the page. Simply click on them to try them out directly. | |
| #""") | |
| #TODO show examples below | |
| with gr.Blocks() as demo: | |
| gr.Markdown(title) | |
| with gr.Row(): | |
| with gr.Column(scale=0.5): | |
| video = gr.Video() | |
| #image = gr.Image(type="filepath") | |
| #gr.Markdown(case_note_upload) | |
| upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary") | |
| clear = gr.Button("Restart") | |
| num_beams = gr.Slider( | |
| minimum=1, | |
| maximum=10, | |
| value=1, | |
| step=1, | |
| interactive=True, | |
| label="beam search numbers)", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.1, | |
| maximum=2.0, | |
| value=1.0, | |
| step=0.1, | |
| interactive=True, | |
| label="Temperature", | |
| ) | |
| audio = gr.Checkbox(interactive=True, value=False, label="Audio") | |
| gr.Markdown(Note_markdown) | |
| with gr.Column(): | |
| chat_state = gr.State() | |
| img_list = gr.State() | |
| chatbot = gr.Chatbot(label='Global-Local QFormer') | |
| text_input = gr.Textbox(label='User', placeholder='Please upload your video first.', interactive=False) | |
| '''with gr.Column(): | |
| gr.Examples(examples=[ | |
| [f"examples/skateboarding_dog.mp4", "What is the dog doing? "], | |
| [f"examples/birthday.mp4", "What is the boy doing? "], | |
| [f"examples/IronMan.mp4", "Is the guy in the video Iron Man? "], | |
| ], inputs=[video, text_input])''' | |
| gr.Markdown(cite_markdown) | |
| upload_button.click(upload_imgorvideo, [video, text_input, chat_state,chatbot], [video, text_input, upload_button, chat_state, img_list,chatbot]) | |
| text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then( | |
| gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list] | |
| ) | |
| clear.click(gradio_reset, [chat_state, img_list], [chatbot, video, text_input, upload_button, chat_state, img_list], queue=False) | |
| #demo.launch(share=False, enable_queue=True, debug=True) | |
| demo.launch(share=False, debug=True) |